A guide to data science for product managers

Artificial intelligence (AI) is an integral part of many modern applications beyond chatbots, influencing areas like credit scoring, social media feeds, and digital security. The roles involved in AI and data-driven product development typically include data engineering, data science, and data analysis, with data scientists focusing on applying machine learning algorithms to solve business problems. These professionals work closely with product managers to ensure that AI tools are effectively integrated into digital products, enhancing their value. Data science addresses various challenges, using structured and unstructured data, and employs supervised and unsupervised learning techniques to generate insights and predictions. Examples of successful AI applications include recommendation systems like Amazon's product suggestions, facial recognition technologies such as Apple's Face ID, and optical character recognition for digitizing documents. Metrics like confusion matrices and AUC-ROC curves are used to evaluate model effectiveness, with product managers playing a crucial role in selecting relevant data and interpreting results. As AI continues to evolve, data scientists are expected to remain pivotal in product development, potentially expanding the traditional product management framework into a "product square" to reflect their importance.